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7.
BEHAVIOURAL PROFILING::CLUSTER ANALYSIS
▸ Finding patterns in behavioural game data
▸ Unsupervised learning strategies to ﬁnd groups/
clusters of players playing in a similar way / ﬁt various
patterns
▸ identify groups with similar behaviour and identify the
most important behavioural features in terms of
underlying patterns in the dataset
Further reading: http://blog.gameanalytics.com/blog/introducing-clustering-
behavioral-proﬁling-game-analytics.html

9.
MAIN CONTRIBUTION
▸ Behavioural proﬁling through clustering with
Archetypal Analysis (AA) combined with progression
analysis in an Open-World game
▸ The main storyline of Just Cause 2 to measure
progression along multiple vectors
▸ Sankey ﬂow diagram for a visual inspection

10.
JUST CAUSE 2
▸ Progression along different vectors, seven Agency-
related missions, missions from a number of Rebel
Factions, Stronghold missions
▸ All mechanics in game available from the beginning
(direct gameplay approach)

12.
FEATURES
▸ Agency missions (+ reach speciﬁc level of Chaos)
▸ subset of features based on the core mechanics
▸ -> does not impact the analytical framework
▸ -> impacts the kinds of conclusions that can be
derived

14.
FEATURES
▸ Spatio-temporal navigation
▸ combat performance
▸ progression through the main storyline
▸ side quests..
▸ Agency missions (+ reach speciﬁc level of Chaos)
▸ subset of features based on the core mechanics
▸ -> does not impact the analytical framework
▸ -> impacts the kinds of conclusions that can be derived

16.
ANALYSIS
▸ Archetypal Analysis (AA) for behavioural proﬁling
▸ AA models applied to all seven agency mission bins
▸ Optimal # of clusters (k) determined for each
(analysis of the residual sum of squares for all k value
less than or equal to 20, and chose the number of
clusters with the elbow criterion)
▸ -> three main archetypes

19.
RESULTS
▸ How does in-game behaviour and performance
change over the various missions?
▸ (see Sankey diagram)
▸ player behaviour changes - players do not
remain in a single cluster (also due to the
nature of the mission design)
▸ domination in exploration-based features
(e.g. playtime)

20.
RESULTS
▸ How many proﬁles enter players on average over the
course of the game?
▸ They change at least once
▸ Avg. 2.91 clusters

21.
RESULTS
▸ How can we describe player behaviour of the different
player proﬁles?

22.
GOALS
• Improve our understanding of the different player
behaviours and factors to improve engagement
• Find issues to avoid drop-outs
• Provide tools for game designers to (visually) analyse
the game and improve the understanding of players
• Find game design ﬂaws early and maybe also
automatically/dynamically